This talk introduces the SQL data source for Flux. It will start with examples of using data from MySQL or Postgres with time series data from InfluxDB. It will then go over the details of how the SQL data source was created.

1. Katy Farmer
Developer Advocate
@thekatertot
Using the SQL
Datasource in 2.0

2. InfluxDB . + Flux + SQL (+ you!)
Multiple Datasources
InfluxDB . + Flux + SQL (+ you!)
Multiple Datasources

3. Why?
• Connect your time series data
with other types of data for
enrichment (and for fun)
• Single responsibility databases
Why?
• Connect your time series data
with other types of data for
enrichment (and for fun)
• Single responsibility databases

4. © InfluxData. All rights reserved.
Industrial IoT
Relational Data
All sensor metrics (temperature, pressure,
water level,
Time Series Data
Hardware information (model, year, etc.), date
deployed, region, id
© InfluxData. All rights reserved.
Industrial IoT
Relational Data
All sensor metrics (temperature, pressure,
water level,
Time Series Data
Hardware information (model, year, etc.), date
deployed, region, id
© InfluxData. All rights reserved.
Industrial IoT
Relational Data
All sensor metrics (temperature, pressure,
water level,
Time Series Data
Hardware information (model, year, etc.), date
deployed, region, id

5. © InfluxData. All rights reserved.
User Tracking
Relational Data
Logins, page visits, clicks, duration on page
Time Series Data
Name, email, location, history (browsing,
purchases, etc.)
© InfluxData. All rights reserved.
User Tracking
Relational Data
Logins, page visits, clicks, duration on page
Time Series Data
Name, email, location, history (browsing,
purchases, etc.)
© InfluxData. All rights reserved.
User Tracking
Relational Data
Logins, page visits, clicks, duration on page
Time Series Data
Name, email, location, history (browsing,
purchases, etc.)

6. Example:
Robots
(Industrial IoT)
Each robot has:
❏ ID
❏ name
❏ model
❏ created_at (timestamp)
Example:
Robots
(Industrial IoT)
Each robot has:
❏ ID
❏ name
❏ model
❏ created_at (timestamp)
Example:
Robots
(Industrial IoT)
Each robot has:
❏ ID
❏ name
❏ model
❏ created_at (timestamp)
Example:
Robots
(Industrial IoT)
Each robot has:
❏ ID
❏ name
❏ model
❏ created_at (timestamp)

7. Example:
Robots
(Industrial IoT)
Robot jobs vary. Some measure parts, some
place parts on belts, and others rotate the
parts for inspection. But not all robots
perform the same. Some of them crash.
What is the average CPU usage of each
robot?
Example:
Robots
(Industrial IoT)
Robot jobs vary. Some measure parts, some
place parts on belts, and others rotate the
parts for inspection. But not all robots
perform the same. Some of them crash.
What is the average CPU usage of each
robot?
Example:
Robots
(Industrial IoT)
Robot jobs vary. Some measure parts, some
place parts on belts, and others rotate the
parts for inspection. But not all robots
perform the same. Some of them crash.
What is the average CPU usage of each
robot?

8. © InfluxData. All rights reserved.
Step : Importing SQL Data with Flux
import "sql"
sql.from(
driverName: "postgres",
dataSourceName: "postgresql://user:password@localhost",
query:"SELECT * FROM mydb;")

9. © InfluxData. All rights reserved.
Step : Importing SQL Data with Flux
import "sql"
sql.from(
driverName: "postgres",
dataSourceName:
"postgresql://localhost/robots?sslmode=disable",
query:"SELECT * FROM robots;")

10. © InfluxData. All rights reserved.© InfluxData. All rights reserved.

11. © InfluxData. All rights reserved.
Step : Write your first Flux query
from(bucket: "robots")
|> range(start: -10d)
|> filter(fn: (r) => r._measurement == "cpu")
|> filter(fn: (r) => r._field == "system_usage")
|> mean()

12. © InfluxData. All rights reserved.© InfluxData. All rights reserved.

13. © InfluxData. All rights reserved.
Step : Joining Data Streams
import "sql"
info = sql.from(
driverName: "postgres",
dataSourceName: "postgresql://localhost/robots?sslmode=disable",
query:"SELECT * FROM robots;")
cpu = from(bucket: "robots")
|> range(start: -10d)
|> filter(fn: (r) => r._measurement == "cpu")
|> filter(fn: (r) => r._field == "system_usage")
|> mean()
join(tables: {cpu:cpu, info:info}, on: ["id"])

14. © InfluxData. All rights reserved.© InfluxData. All rights reserved.

15. © InfluxData. All rights reserved.
Step : Storing Results
sql.to() requirements:
❏ Data in the stream must have same column names as the
SQL DB
❏ Drop any columns that won’t be stored
❏ Remember your schema! Any rules (not null, unique, etc.)
must be followed or the write will not succeed.

16. © InfluxData. All rights reserved.
import "sql"
info = sql.from(
driverName: "postgres",
dataSourceName: "postgresql://localhost/robots?sslmode=disable",
query:"SELECT * FROM robots;")
cpu = from(bucket: "robots")
|> range(start: -10d)
|> filter(fn: (r) => r._measurement == "cpu")
|> filter(fn: (r) => r._field == "system_usage")
|> mean()
join(tables: {cpu:cpu, info:info}, on: ["id"])
|> keep(columns: ["id", "_value", "created_at"])
|> sql.to(
driverName: "postgres",
dataSourceName: "postgresql://localhost/robots?sslmode=disable",
table: "averages")

17. © InfluxData. All rights reserved.
Things I did wrong
© InfluxData. All rights reserved.
Things I did wrong

18. © InfluxData. All rights reserved.
Questions
Thank you!
katy@influxdata.com
@thekatertot
© InfluxData. All rights reserved.
Questions
Thank you!
katy@influxdata.com
@thekatertot
© InfluxData. All rights reserved.
Questions
Thank you!
katy@influxdata.com
@thekatertot
© InfluxData. All rights reserved.
Questions
Thank you!
katy@influxdata.com
@thekatertot
© InfluxData. All rights reserved.
Questions
Thank you!
katy@influxdata.com
@thekatertot